Character recognition has been applied to cursive script and printed characters which are written on a tablet, electronic or otherwise, and transformed into an image for input to a neural network. Such an image may be a static ("off-line") image of the character or it may be a dynamic ("on-line") image of the character. The former is a set of x and y coordinates with associated brightness levels whereas the latter is a temporal ordering of the x and y coordinate information together with any additional dynamic stroke information such as pen pressure and the like.
Neural networks have been designed to recognize either type of image for a character with varying degrees of success. They are sensitive to changes in appearance of a character presented for classification. These networks prefer that the characters adhere to some degree of consistency from one writer to the next. Placement of the character within a writing area, size of the character, and attitude of the character as well as other handwritten character attributes all contribute to the network's ability to correctly classify the character.
Several systems dealing with recognition of on-line handwritten characters are described in U.S. Pat. Nos. 4,317,109 and 4,653,107. These systems depend on segmentation of the character into a plurality of stroke segments which are then compared with a reference stroke for likeness and relative position in the order of stroke segments. Essentially, these techniques involve template matching with an additional factor relating to the sequential ordering of the segments. Such approaches may tend to be applicable to Chinese and Japanese characters but they suffer from an inability to correctly recognize other cursive and handprinted characters when executed by different writers.
Other systems proposed in the literature depend heavily on such factors as: limiting the number of recognizable classes of characters to digits only, uppercase or lowercase letters only, or symbols only; restricting character formation to be either isolated characters or continuous character groups; and using syntactic, semantic or context information to aid in the classification and recognition processes.